Summary of The Reliability Paradox: Exploring How Shortcut Learning Undermines Language Model Calibration, by Geetanjali Bihani and Julia Rayz
The Reliability Paradox: Exploring How Shortcut Learning Undermines Language Model Calibration
by Geetanjali Bihani, Julia Rayz
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers investigate the relationship between pre-trained language models’ (PLMs) reliability and their tendency to use shortcuts. They find that PLMs with lower calibration errors, often considered more reliable, actually employ non-generalizable decision rules. This challenges the assumption that well-calibrated models are inherently reliable. The study emphasizes the need for frameworks that balance model calibration and generalization objectives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how pre-trained language models work. Scientists want to know if these models can make good predictions. They look at something called calibration error, which tells them if the models are confident in their answers. Surprisingly, they find that some models seem better than others because they’re using shortcuts. This means they’re not really making smart decisions. The researchers think we need to do a better job of balancing how well our models work and how good they are at making predictions. |
Keywords
» Artificial intelligence » Generalization
